How reliably can algorithms identify eosinophilic asthma phenotypes using non‐invasive biomarkers?

Abstract Background and Aims Asthma is a heterogeneous respiratory disease that encompasses different inflammatory and functional endophenotypes. Many non‐invasive biomarkers has been investigated to its pathobiology. Heany et al proposed a clinical algorithm that classifies severe asthmatic patients into likely‐eosinophilic phenotypes, based on accessible biomarkers: PBE, current treatment, FeNO, presence of nasal polyps (NP) and age of onset. Materials and Methods We assessed the concordance between the algorithm proposed by Heany et al. with sputum examination, the gold standard, in 145 asthmatic patients of the MEGA cohort with varying grades of severity. Results No correlation was found between both classifications 0.025 (CI = 0.013–0.037). Moreover, no relationship was found between sputum eosinophilia and peripheral blood eosinophilia count in the total studied population. Discussion and Conclusion In conclusion, our results suggest that grouping the biomarkers proposed by Heany et al. are insufficient to diagnose eosinophilic phenotypes in asthmatic patients. Sputum analysis remains the gold standard to assess airway inflammation.


Results:
No correlation was found between both classifications 0.025 (CI = 0.013-0.037). Moreover, no relationship was found between sputum eosinophilia and peripheral blood eosinophilia count in the total studied population.

Discussion and Conclusion:
In conclusion, our results suggest that grouping the biomarkers proposed by Heany et al. are insufficient to diagnose eosinophilic phenotypes in asthmatic patients. Sputum analysis remains the gold standard to assess airway inflammation.

K E Y W O R D S
asthma, biomarkers, eosinophils, exhaled nitric oxide, non-eosinophilic, phenotypes, sputum

| INTRODUCTION
Asthma is a respiratory syndrome characterised by airway inflammation and reversible airway obstruction. 1 Due to its heterogeneity, considerable efforts have been made to subclassify the disease into different phenotypes and identify non-invasive biomarkers that reflect its pathobiology. Sputum examination is the gold standard for determining airway inflammation; other non-invasive biomarkers studied to date, such as peripheral blood eosinophil (PBE) count, serum periostin, fraction of exhaled nitric oxide (FeNO), and serum IgE levels, have low specificity and sensitivity. 2  We analysed the consistency between the Heany et al. algorithm 3 and sputum examination in a retrospective analysis of asthmatic patients with varying degrees of severity from eight Spanish hospitals, previously described as the MEGA cohort. 4 As secondary outcomes, we evaluated the clinical characteristics, asthma severity, and lung function in these phenotypes. 4,5

| MATERIAL AND METHODS
Patients from the MEGA cohort with a valid sputum analysis and an accurate asthma diagnosis were selected. 4

| RESULTS
Data from 145 asthmatic subjects aged 18-75 years were categorised according to the phenotypes proposed by Heany et al. 3 Grade 3 (likely eosinophilic) was the most prevalent (69.6%), followed by grade 2 (likely eosinophilic; 20.8%), grade 1 (less likely; 16.8%); and grade 0 (non-eosinophilic; 5.9%). The average patient age was 48 years, and a majority were female. There was no significant difference in demographic characteristics, asthma severity, exacerbations, or lung function between grades. As FeNO, PBE, and NP were classification criteria, higher levels were shown in grade 3 (p < 0.05).
Data are summarised in Table 1.
The agreement between the eosinophilia grades proposed by Though the sputum eosinophilia rate was higher in grade 3, it was not statistically significant compared to the other grades (see Table 1 and Figure 1). We examined the relationship of sputum eosinophils with PBE count, finding no significant correlation in the total population (r = 0.11, p = 0.22). No correlation was found considering different grades (r = −0.17 (p = 0.93), r = 0.08 (p = 0.78), r = 0.35 (p = 0.12), and r = 0.16 (p = 0.16)) for grades 0, 1, 2, and 3, respectively.

| DISCUSSION
Several biomarkers have been considered to phenotype asthmatic patients. Heany et al. proposed a system of eosinophilic probability depending on easily accessible biomarkers. 3 The low agreement   Significant difference between both. c Significant differences were determined between all inter-group analysis except 0-1. d Significant differences were determined between grade 0-3 and 1-3. 1 Obesity was defined as BMI over 30 kg/m 2 .
found in our study between this algorithm and sputum analysis suggests no relationship between the two criteria.
We observed higher sputum eosinophilia levels in grade 3 of Heany's classification, but without reaching statistical significance.
The percentage of patients with sputum eosinophils >3% was similar in grades 2-3 (eosinophilic) and 1, indicating an inability of the proposed system to phenotype these patients.
As in other studies showing a 70%-80% prevalence of the eosinophilic phenotype in tertiary centers, 3,6 the 69.6% rate in our study contrasts with the 50% previously proposed. 1 Furthermore, our study found a 5.9% prevalence of non-T2 asthma, similar to the 5% reported by Kerkhof et al. 7 Worse lung function is associated with eosinophilic phenotypes, 6,8,9 as demonstrated in our previous report characterising MEGA patients. 5 No differences in lung function were found in the present study, likely owing to the presence of sputum eosinophilia in all grades.
A clear correlation has been reported between eosinophilic asthma and greater severity and exacerbations. 6,8,9 Higher exacerbation rates and severity were found in the "eosinophilic grades" of our study, without reaching a statistical difference (p = 0.07). Detecting intact eosinophils only, as in our study, may give misleading information on the real prevalence of eosinophilic sputum, marking one limitation of our study. Further limitations include a possible bias toward recruiting predominantly eosinophilic patients in tertiary centres and allergy clinics, which could be confounding given the limitations in finding significant differences in unequally sized groups.
In conclusion, our results suggest that the biomarker groupings